www.aging-us.com 10715 AGING
INTRODUCTION
The most prominent characteristic of Type 2 Diabetes
(T2D) is the abnormally high plasma glucose levels
(hyperglycemia), which can lead to complications
including cardiovascular morbidity, renal impairment,
retinopathy [1, 2]. The incidence of T2D is highly
correlated with age [3, 4]. Absorption of plasma
glucose by peripheral tissues requires insulin, which is
secreted by β-cells in the pancreas [5]. β-cell
dysfunction is well recognized to directly cause
diabetes; lack of β-cell due to autoimmune disease is
the major cause of type 1 diabetes (T1D) [6]. In T2D
however, hyperglycemia is generally resulted from
more complex factors including compromised function
of β-cells and impaired insulin response of peripheral
tissues, all of which are attributes of aging [5, 7]. The
human insulin is a peptide composed of 51 amino
acids (AA) processed from a premature 86-AA peptide
called proinsulin in β-cells [8, 9]. Proinsulin is
synthesized in the endoplasmic reticulum (ER), then
transported to Golgi apparatus where posttranslational
modifications and maturation happen [10, 11]. The
matured insulin is then secreted into the plasma via
intracellular vesicles. A significant fraction of pro-
insulin in the plasma remain uncleared and their
function remains currently unknown [12, 13]. Studies
suggest that approximately 10–20% of the circulating
IRI are proinsulin and proinsulin is cleared from the
plasma slower than mature insulin [14, 15]. The
proinsulin levels and the proinsulin to insulin ratio (P/I
ratio) are increased during aging [16].
www.aging-us.com AGING 2020, Vol. 12, No. 11
Research Paper
Subgroup analysis of proinsulin and insulin levels reveals novel correlations to metabolic indicators of type 2 diabetes
Tangying Li1, Huibiao Quan2, Huachuan Zhang3, Leweihua Lin2, Lu Lin2, Qianying Ou2, Kaining Chen2 1Department of Health Care Centre, Hainan General Hospital, Haikou 570311, Hainan, China 2Department of Endocrinology, Hainan General Hospital, Haikou 570311, Hainan, China 3Department of Endocrinology Laboratory, Hainan General Hospital, Haikou 570311, Hainan, China
Correspondence to: Huibiao Quan; email: [email protected] Keywords: diabetes, aging, glucose, proinsulin, insulin Received: December 11, 2019 Accepted: April 27, 2020 Published: June 12, 2020
Copyright: Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Proinsulin, insulin and proinsulin/insulin (P/I) ratio have been reported to be correlated with fasting plasma glucose (FPG) and Hemoglobin A1c (HbA1c) in whole population study therefore sensitive predictors of T2D progression. However, by analyzing data collected from 2018-2019 from a cohort of 1579 East Asian individuals from Hainan Province of China, we find that the associations of proinsulin, insulin and P/I ratio with diabetic indicators have distinct, sometimes opposite regression patterns in normal, prediabetic and diabetic subgroups. The strength of the associations are generally weak in normal and prediabetic groups, and only moderate in diabetic group between postprandial proinsulin and HbA1c, between postprandial insulin and FPG or HbA1c, and between postprandial P/I ratio and FPG or HbA1c. Receiver operating characteristic (ROC) curve analysis shows these parameters are weaker than age in predicting diabetes development, with P/I ratio being the weakest. Proinsulin and insulin levels are tightly associated with insulin sensitivity across all subgroups, as measured by Matsuda index. Together, our results suggest that proinsulin, insulin or P/I ratio are weak predictors of diabetes development in the whole population, urging the need for stratifying strategies and novel perspectives in evaluating and predicting hyperglycemia progression.
www.aging-us.com 10716 AGING
Hyperproinsulinemia, a term describing the abnormal
elevation of plasma proinsulin levels, has been
proposed to be a predictor of subsequent development
of both type 1 and type 2 Diabetes [15, 17]. In type 2
diabetes, the proinsulin levels are shown to be as high
as mature insulin levels, indicating a defect in insulin
processing [15, 17]. Other studies also find that fasting
proinsulin levels are associated with insulin resistance
[18] and type 2 diabetes [19–21]. Furthermore, non-
diabetic twins of patients with T1D also show elevated
proinsulin concentrations, suggesting that proinsulin is
an indication of early subclinical β-cell dysfunction
[17]. The P/I ratio has also been proposed as a
predictor of hyperglycemia development. The P/I ratio
at the fasting condition is highly responsive to acute
insulin stimulation [22]. Similarly, Haffner et al.
show that the P/I ratio is increased in individuals
having insulin resistance syndrome [23]. In a most
recent survey of 9396 Finnish men, both fasting and
glucose-stimulated proinsulin levels are significantly
associated with plasma glucose levels and insulin
sensitivity [24].
However, other studies find that the elevation of
proinsulin levels is a late event after manifestation of
hyperglycemia [25]. Supporting this, glucose can
stimulate proinsulin production in a dose-dependent
manner [26], arguing against the idea that defect in
proinsulin processing is causal to hyperglycemia and
T2D. Another possible cause of elevated proinlusin
levels is that, in response to high glucose, insulin
secreting cells may try to speed insulin production,
causing proinsulin to be incompletely processed and
secreted to the plasma [27]. In addition, there are many
inconsistent results regarding the relationship between
P/I ratio and glucose tolerance. In insulin resistant,
nondiabetic subjects, the fasting P/I ratio had no
correlation with the degree of insulin resistance, despite
the both are increased in these subjects [28].
In this study, we obtained multiple health parameters
from 1579 individuals in Hainan Province, China from
2018-2019. FPG, HbA1C levels and plasma glucose
levels after 2-hour oral glucose tolerance test
(OGTT2hPG) were used to group them into normal,
prediabetic and diabetic populations. We measured the
proinsulin, insulin levels at both fasting and glucose-
stimulated conditions and examined their correlations to
diabetic indicators including FPG, HbAc1 and
OGTT2hPG, and the insulin sensitivity index Matsuda
index. We find that none of proinsulin, insulin or P/I
ratio has strong correlation to these diabetic indicators
in the whole population. By subgrouping analysis, we
find that the correlations are distinct and sometimes
opposite in normal, prediabetic and diabetic popula-
tions. Our results provide new perspectives regarding
the functions of proinsulin, insulin and P/I ratio in
predicting hyperglycemia development.
RESULTS
In a study surveying the health information in provincial
area of Hainan, China, we aimed to recruit over one
thousand adults with diverse backgrounds and measure
their physiological and metabolic parameters. We also
conducted the 2-hour glucose tolerance test (OGTT)
and measured the postprandial proinsulin, insulin and
glucose levels in the plasma. The data collected from
1579 individuals were complete and valid, which were
summarized in Supplemental Information, Sup-
plementary Table 1. The original data can be found in
Supplementary Data (Supplementary evidence of
original data). The processed data can be found in
Supplementary Table 2.
We divided the whole population into 3 groups: normal,
diabetes and prediabetes according to the standard set by
American Diabetes Association [31, 32]: diabetes, fasting
plasma glucose (FPG) ≥7.0 mmol/L or oral glucose
tolerance test (OGTT) with 2-hour plasma glucose
(2hPG) ≥11.1 mmol/L or HbA1c ≥6.5%; prediabetes,
5.6mmol/L ≤ FPG<7.0 mmol/L or 7.8mmol/L ≤
2hPG<11.1mmol/L or 5.7% ≤ HbA1C<6.4%; otherwise
normal. Among the participants, 639 (40.5%) were
normal, 705 (44.6%) were prediabetic and 235 (14.9%)
were diabetic. The average age was 41.69 years for the
normal group, 50.91 for prediabetic group and 56.75 for
diabetic group, confirming the age-dependence of T2D
(Supplementary Table 1). Other physiological and
metabolic parameters including weight, height, body
mass index (BMI), waist circumference, systolic and
diastolic pressure, heartbeat, FPG, postprandial glucose
(OGTT2hPG), triglyceride, total cholesterol, LDL, HDL,
blood urine acid, HbA1c, Vitamin D3, fasting proinsulin
levels, postprandial proinsulin levels (2h proinsulin),
fasting insulin levels and postprandial proinsulin levels
(2h insulin) were all averaged and compared among the
normal, prediabetic and diabetic groups (Supplementary
Table 1). The subgroups were further divided to male and
female groups and physiological and metabolic
parameters were compared in the subgroups.
Proinsulin, insulin and proinsulin to insulin (P/I)
ratio in normal, prediabetic and diabetic population
The fasting proinsulin levels and fasting insulin levels
were all gradually increased from normal to diabetic
group (Figure 1A, 1B). Fasting proinsulin increased
from 11.56±8.14 to 13.47±10.63 pmol/L in prediabetes
and 21.91±23.82 pmol/L in diabetes. Similarly, fasting
insulin levels also increased from 57.53±34.03 to
67.62±44.18 pmol/L in prediabetes and 86.69±84.
www.aging-us.com 10717 AGING
84 pmol/L in diabetes (Supplementary Table 1).
However, the fasting P/I ratio was not significantly
increased from normal to prediabetic group and
only marginally from prediabetic to diabetic group
(Figure 1C). After 2 hours of glucose stimulation in the
OGTT, proinsulin levels were not significantly different
between normal (57.80±50.26 pmol/L) and prediabetic
group (62.99±51.66 pmol/L), but slightly increased in
diabetic group (74.52±63.44 pmol/L) (Figure 1D and
Supplementary Table 1). Postprandial insulin levels were
Figure 1. Plasma proinsulin, insulin levels and proinsulin to insulin (P/I) ratio in normal, prediabetic and diabetic groups. (A) Fasting proinsulin levels graduately increased from normal to prediabetic and diabetes population. A cohort of 1579 participants were grouped to normal, prediabetes and diabetes according to the standard set by American Diabetes Association: diabetes, fasting plasma glucose (FPG) ≥7.0 mmol/L or oral glucose tolerance test (OGTT) with 2-hour plasma glucose (2hPG) ≥11.1 mmol/L or HbA1c ≥6.5%; prediabetes, 5.6mmol/L≤FPG<7.0 mmol/L or 7.8mmol/L≤2hPG<11.1mmol/L or 5.7%≤HbA1C<6.4%; otherwise normal. Error bars: standard deviation. Student’s t-test: ****, P<0.0001. (B) Fasting insulin levels graduately increased from normal to prediabetic and diabetes population. Participants were grouped and data were analyzed as in (A). Student’s t-test: ****, P<0.0001. (C) Fasting proinsulin to insulin ratio (P/I ratio) had no difference between normal andes groups and only slight increase in diabetic group. Participants were grouped and data analyzed as in (A). Student’s t-test: *, P<0.05; ns, not significant. (D) Proinsulin levels after 2-hour glucose stimulation in an oral glucose tolerance test (2hOGTT) were significantly elevated in diabetes but not prediabetic groups. Student’s t-test: **, P<0.01; ns, not significant. (E) Insulin levels after 2-hour glucose stimulation in an OGTT were significantly elevated in prediabetic group but did not further increase in diabetic group. Student’s t-test: **, P<0.01; ns, not significant. (F) After 2hOGTT, proinsulin to insulin ratio (P/I ratio) had no difference between normal and diabetic groups but was significantly lower in prediabetic group. Student’s t-test: ****, P<0.0001; ns, not significant. (G) 2-hour glucose stimulation did not increase proinsulin levels in prediabetic and diabetic groups. Student’s t-test: ns, not significant. (H) 2-hour glucose stimulation increased Insulin levels in prediabetic group but did not further increase in diabetic group. Student’s t-test: ****, P<0.0001; ns, not significant.
www.aging-us.com 10718 AGING
significantly increased from normal (379.99±295.46
pmol/L) to prediabetic group (572.72±491.65 pmol/L)
but were not further increased in diabetic group
(600.48±570.17 pmol/L). The changes in the proinsulin
and insulin levels after 2-hour OGTT resulted in a
decrease in P/I ratio from normal to prediabetic group but
increase from prediabetic to diabetic group (Figure 1F).
The lack of pronounced increase in P/I ratio suggests that
the proinsulin processing has no significant defect in
prediabetic and diabetic population.
2-hour glucose stimulation increased the proinsulin
levels to around 5~10 folds in all subgroups (Figure 1G).
However, 2-hour glucose stimulation increased the
insulin levels significantly higher in prediabetic and
diabetic group (Figure 1H), suggesting that insulin
production remains sensitive to glucose stimulation and
is not the major reason for hyperglycemia in type 2
diabetes in this cohort.
The correlation of proinsulin and insulin with FPG
were different and sometimes opposite in normal,
prediabetic and diabetic groups
Several previous studies have shown the association of
proinsulin, insulin and the P/I ratio with FPG and
suggest their applications in predicting T2D
development [18, 24, 33]. We found that in the whole
population examined here, all associations were
generally weak (Supplementary Figure 1). By using
spearman ranking, the association coefficient Rho is
0.266 between fasting proinsulin and FPG, 0.159
between glucose-stimulated proinsulin and FPG, 0.232
between fasting insulin and FPG, no significant
association between glucose-stimulated insulin and
FPG, 0.068 between fasting P/I ratio and FPG, no
significant association between glucose-stimulated P/I
ratio and FPG. By comparing normal, prediabetic and
diabetic groups on the same scatter plot (red, green and
blue, respectively), we noticed that the three groups
have very different distribution. In diabetic group,
glucose-stimulated proinsulin and insulin levels were
not trending in the same way as those in normal and
prediabetic group (Supplementary Figure 1B, 1D).
We then studied the association in each subgroup.
Indeed, many associations were quite different among
normal, prediabetic and diabetic groups (Figure 2).
Although the associations with FPG had different
strength (Rho) in different subgroups, they were mostly
trending the same for fasting proinsulin (Figure 2A),
fasting insulin (Figure 2C), fasting P/I ratio (Figure 2E)
and glucose-stimulated P/I ratio (Figure 2F). However,
for glucose-stimulated proinsulin and insulin levels, the
associations to FPG were opposite, with positive
association in normal and prediabetic group but negative
association in diabetic group (Figure 2B, 2D). The
strength of associations with FPG was also very
different. Despite mostly weak associations in normal
and prediabetic group, in diabetic group, FPG had
moderate association with glucose-stimulated insulin
levels (Figure 2D, Rho = -0.446) and glucose-stimulated
P/I ratio (Figure 2F, Rho = 0.401), with the regression
coefficient of β = -0.350 and 0.448, respectively (Table 1).
The association remained significant after adjusting for
sex, age and body mass index (BMI) (Table 1).
Hemoglobin A1c was negatively correlated with
glucose-stimulated proinsulin and insulin levels in
type 2 diabetes
HbAc1 levels is currently used to diagnose T2D [34, 35].
However, in our study, the HbA1c associations with
proinsulin, insulin or P/I ratio in the fasting stage or after
2 hours of glucose stimulation were very weak or not
significant in the whole population (Supplementary
Figure 2). We then compared the correlations in normal,
prediabetic and diabetic groups (Figure 3). In normal
group, HbAc1 had no significant association with all
parameters examined. In prediabetic group, only weak
HbA1c associations were observed for glucose-
stimulated proinsulin and insulin (Figure 3B, 3D, Rho = -
0.203 and Rho = -0.106, respectively), resulting in a low
association of glucose-stimulated P/I ratio to HbA1c
(Rho = -0.144) (Figure 3F). However, in diabetic group,
weak HbAc1 associations were found for fasting insulin
and fasting P/I ratio (Figure 3C, 3E, Rho = -0.132, Rho=
0.154, respectively). Medium HbA1c association (Rho =
-0.346) was found for glucose-stimulated proinsulin and
close to strong association (Rho = -0.589) for glucose-
stimulated insulin levels (Figure 3B, 3D). Interestingly,
this resulted in a positive correlation of HbA1c with
glucose-stimulated P/I (Rho = 0.305) in diabetic group,
contrasting to the negative correlation in prediabetic
group and maybe normal group as well (Figure 3F).
Regression analysis and adjustment were conducted for
medium to strong associations, It appeared that the
association remained significant after adjusting for sex,
age and BMI (Table 1).
Proinsulin and insulin correlation with postprandial
glucose in normal, prediabetic and diabetic groups
Postprandial glucose levels, e.g. plasma glucose levels
after 2 hour of glucose stimulation in an oral glucose
tolerance test (OGTT2hPG), is one of the three
indicators used to diagnose T2D [36]. We studied the
correlation of this parameter with proinsulin and insulin
levels at fasting stage and after glucose stimulation.
In the whole population, there were weak associations
of OGTT2hPG with fasting proinsulin levels
(Supplementary Figure 3A, rho = 0.164), postprandial
www.aging-us.com 10719 AGING
proinsulin levels (Supplementary Figure 3B, Rho =
0.194) and fasting insulin levels (Supplementary Figure
3C, Rho = 0.164). There was a moderate association
between OGTT2hPG with postprandial insulin levels
(Supplementary Figure 3D, Rho = 0.434), with a
regression coefficient β = 0.223 (Table 1). The
association remained significant after adjusting for age,
sex and BMI (Table 1). The association of fasting P/I
ratio with OGTT2hPG was not significant, however,
there was a negative association between postprandial
P/I ratio and OGTT2hPG (Supplementary Figure 3F,
Rho = -0.228).
Interestingly, the patterns of scatter plot of diabetic
groups were quite different from normal and prediabetic
groups in some cases (Supplementary Figure 3D–3F),
prompting us to study the correlation in the subgroups.
Importantly, although the correlations in subgroups
generally agreed with that of the whole population
(Figure 4A, 4B, 4E), there were some novel observations.
Both normal and prediabetic groups had moderate,
positive associations between postprandial insulin levels
and OGTT2hPG (Figure 4D, Rho = 0.460 and 0.533,
respectively). The regression coefficient β were 0.408 and
0.423 for normal and prediabetic groups, respectively,
Figure 2. Nonuniform correlations of proinsulin, insulin and P/I ratio with fasting plasma glucose (FPG) in normal, prediabetic and diabetes population. (A) Correlation of fasting proinsulin levels with FPG in normal, prediabetic and diabetes populations. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language for each subgroup (normal in red, prediabetes in green and diabetes in blue). Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software, with absolute Rho < 0.3 considered weak, 0.3≤Rho<0.6 moderate and Rho≥0.6 strong association; ns, not significant. Shaded line, linear regression with 95% of confidence interval (CI). (B) Distinct correlation of proinsulin levels after 2hOGTT to FPG levels in normal, prediabetic and diabetic groups. (C) Correlation of fasting insulin levels with fasting plasma glucose (FPG) in normal, prediabetic and diabetes populations. (D) Opposite correlation of 2h OGTT insulin levels to FPG levels in normal and diabetes subgroups. In diabetes, 2h OGTT insulin levels has negative correlation to FPG with moderate association strength (Rho = -0.446). (E, F) The strength of association (Rho) of P/I ratio were different among normal, prediabetic and diabetic groups. In diabetes, 2h OGTT P/I ratio has positive correlation to FPG with moderate association strength (Rho = 0.401).
www.aging-us.com 10720 AGING
Table 1. Regression study of the moderate and strong associations for proinsulin and insulin at fasting and glucose-stimulating conditions.
Variable Associated
variable Subgroup
Association
coefficient (Rho)
Regression
coefficient (β) P P1 P2
Matsuda ISI All -0.463 -0.191 < 0.001 < 0.001 < 0.001
Matsuda ISI Normal -0.384 -0.177 < 0.001 < 0.001 < 0.001
Matsuda ISI Prediabetic -0.462 -0.248 < 0.001 < 0.001 < 0.001
Matsuda ISI Diabetic -0.550 -0.346 < 0.001 < 0.001 < 0.001
Matsuda ISI All -0.830 -0.301 < 0.001 < 0.001 < 0.001
Matsuda ISI Normal -0.837 -0.309 < 0.001 < 0.001 < 0.001
Matsuda ISI Prediabetic -0.846 -0.416 < 0.001 < 0.001 < 0.001
Matsuda ISI Diabetic -0.903 -0.460 < 0.001 < 0.001 < 0.001
HbA1c Diabetic -0.346 -0.225 < 0.001 < 0.001 < 0.001
Matsuda ISI All -0.463 -0.206 < 0.001 < 0.001 < 0.001
Matsuda ISI Normal -0.412 -0.163 < 0.001 < 0.001 < 0.001
Matsuda ISI Prediabetic -0.480 -0.269 < 0.001 < 0.001 < 0.001
Matsuda ISI Diabetic -0.539 -0.396 < 0.001 < 0.001 < 0.001
FPG Diabetic -0.446 -0.350 < 0.001 < 0.001 < 0.001
HbA1c Diabetic -0.589 -0.418 < 0.001 < 0.001 < 0.001
OGTT2PG All 0.434 0.223 < 0.001 < 0.001 < 0.001
OGTT2PG Normal 0.460 0.408 < 0.001 < 0.001 < 0.001
OGTT2PG Prediabetic 0.533 0.423 < 0.001 < 0.001 < 0.001
Matsuda ISI All -0.822 -0.294 < 0.001 < 0.001 < 0.001
Matsuda ISI Normal -0.844 -0.269 < 0.001 < 0.001 < 0.001
Matsuda ISI Prediabetic -0.876 -0.384 < 0.001 < 0.001 < 0.001
Matsuda ISI Diabetic -0.725 0.483 < 0.001 < 0.001 < 0.001
FPG Diabetic 0.401 0.408 < 0.001 < 0.001 < 0.001
HbA1c Diabetic 0.305 0.331 < 0.001 < 0.001 < 0.001
The association coefficient (Rho) was obtained by spearman method by using SPSS software. Linear regression was applied to derive the regression coefficient (β) and ANOVA was applied to derive the statistical significance (P value). P1 was adjusted for age, sex and P2 age, sex and BMI. Abbreviation: FPG, free plasma glucose. OGTT2PG, plasma glucose levels after 2 hours of oral glucose tolerance test (OGTT).
and the associations remained significant after adjusting
for age, sex and BMI (Table 1). In contrast, the diabetic
group did not show a significant association between
postprandial insulin and OGTT2hPG, although trending a
negative correlation (Figure 4D). The fasting P/I ratio
became significantly associated with OGTT2hPG in
diabetic groups (Figure 4E, Rho = 0.172), but not in the
whole population (Supplementary Figure 3E). Again, in
normal and prediabetic groups, glucose-stimulated P/I
ratio were negatively correlated with OGTT2hPG but
was trending positively in diabetic group (Figure 4F).
Tight association of proinsulin levels with Matsuda
index across normal, prediabetic and diabetic
subgroups
The Matsuda index is composite index for insulin
sensitivity, which has been widely used as an important
measurement of glucose intolerance and hyperglycemia
development [29, 30]. The Matsuda index in this study
was calculated from fasting and postprandial glucose and
insulin levels in a 2-hour OGTT. Not surprisingly,
Matsuda index were highly associated with insulin levels
at both fasting stage (Rho = -0.83) and after 2-hour
glucose stimulation (Rho = -0.822) in the whole
population (Supplementary Figure 4C, 4D). Interestingly,
proinsulin levels also had a very good association with
Matsuda index: both fasting and postprandial proinsulin
levels correlated with Matsuda index with Rho = -0.463
(Supplementary Figure 4A, 4B). However, the P/I ratio at
both fasting stage and after 2-hour glucose stimulation
showed only weak associations with Matsuda index
(Supplementary Figure 4E, 4F). In subgroup analysis,
surprisingly, all subgroups followed the same trend as the
whole population (Figure 5), although proinsulin levels
were slightly better associated with Matsuda index in
diabetic group, as compared with those in normal and
prediabetic groups. Similarly, all subgroups showed only
www.aging-us.com 10721 AGING
weak associations between P/I ratio and Matsuda index
(Figure 5E, 5F). All moderate (Rho ≥0.3) to strong
associations (Rho ≥0.6) remained significant after
adjustment for sex, age and BMI (Table 1). Regression
coefficient β was also calculated for moderate and strong
associations (Table 1).
Weak prediction power of proinsulin, insulin and P/I
ratio for diabetes in the whole population
The weak association of proinsulin, insulin and P/I ratio
with multiple diabetic indicators in the whole population
suggest that they are not good predictor of diabetes
development. To confirm this possibility, we used the
widely applied Receiver Operating Characteristic (ROC)
curve to evaluate prediction power of proinsulin, insulin
and P/I ratio. As shown in Figure 6, a better predictor
such as HbA1c was curving to the left upper corner. Our
results showed that, compared to age, which is well
correlated to FPG and HbA1c and a moderate predictor
of diabetes, all proinsulin, insulin and P/I ratio were
weak predictors for diabetes (Figure 6A). After 2-hour
glucose stimulation, the proinsulin, insulin and P/I
ratio remained weak predictors for diabetes (Figure 6B).
Figure 3. Nonuniform correlations of proinsulin, insulin and P/I ratio with fasting hemoglobin A1C (HbA1c) in normal, prediabetic and diabetic groups. (A) Fasting proinsulin levels were not significant associated HbA1c in all groups of normal, prediabetes and diabetes. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language for each subgroup (normal in red, prediabetes in green and diabetes in blue). Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software: ns, not significant. Shaded line, linear regression with 95% of confidence interval (CI). (B) Negative association of proinsulin levels after 2 hours of glucose stimulation with fasting HbA1c. The association is stronger in diabetic group (Rho = 0.346) than in normal and prediabetic groups. (C) Fasting insulin levels had weak and negative association with fasting HbA1c in diabetic group but not normal and prediabetic groups. (D) Insulin levels after 2 hours of glucose stimulation had no correlation with fasting HbA1c in normal group, weak association in prediabetic group and close to strong association in diabetic group (Rho= -0.589). (E) Fasting P/I ratio and fasting HbA1c were weakly associated in diabetic group not but normal and prediabetic groups. (F) P/I ratio after 2 hours of glucose stimulation was moderately associated with fasting HbA1c in diabetic group, weakly in prediabetic group but not significant in normal group.
www.aging-us.com 10722 AGING
Interestingly, the P/I ratio in both fasting and
postprandial conditions was the least effective predictor
of diabetes, contrasting to some previous studies. These
results were consistent with our association studies in the
whole population, which suggest that, in the whole
population, the proinsulin, insulin and P/I ratio are not
powerful predictors of diabetes.
DISCUSSION
Previous proposals of using proinsulin, insulin and P/I
ratio to predict diabetes development are based on the
assumptions that they are strongly or at least moderately
correlated to diabetes indictors such as FPG, HbA1c and
insulin sensitivity. By plotting all the data points in the
correlation study, we find that most correlations are too
weak to support their predicting function. Interestingly,
we notice that subsets of data points do not follow a
linear model. This prompts us to subgroup them into
normal, prediabetic and diabetic population. By dividing
1579 individuals into subgroups, our study reveals
distinct sometimes opposite correlations in different
subgroups for the same parameters. Our studies also
reveal unexpected correlations of proinsulin and insulin
Figure 4. Comparison of associations of proinsulin and insulin levels with plasma glucose levels after 2 hours glucose stimulation in an oral glucose tolerance test (OGTT2hPG) in normal, prediabetic and diabetic groups. (A) Fasting proinsulin levels was better associated with OGTT2hPG in diabetic group than in normal and prediabetic group. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language for each subgroup (normal in red, prediabetes in green and diabetes in blue). Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software: ns, not significant. Shaded line, linear regression with 95% of confidence interval (CI). (B) Proinsulin levels after OGTT was better associated with OGTT2hPG in normal and prediabetic groups (Rho = 1.58 and 0.246, respectively) than in diabetic group. (C) Weak association of fasting insulin levels with OGTT2hPG in normal and prediabetic groups but no significant association in diabetic group. (D) Insulin levels after 2h OGTT had moderate association with OGTT2hPG in normal and prediabetic groups but no in diabetic group. (E) Fasting P/I ratio had weak association with OGTT2hPG in diabetic group but no in normal and prediabetic group. (F) Glucose-stimulated P/I ratio had moderate negative association with OTGG2hPG in normal and prediabetic groups but not in diabetic group.
www.aging-us.com 10723 AGING
with diabetic indicators in some subgroups. Since such
associations have not been systematically compared in
normal, prediabetic and diabetic groups, our studies
could raise further interests in using subgroup analysis in
similar studies.
Weak predicting power of proinsulin, insulin and P/I
ratio for type 2 diabetes development
Proinsulin levels, insulin levels and P/I ratio have been
shown to be associated with diabetes parameters with
various strength in different cohorts [19, 37–41]. We
examine in our cohort the correlations and find that,
although proinsulin levels, insulin levels and P/I ratio are
correlated with diabetes parameters similar to those
reported in the literature, the strength of association are
generally weak, with Spearman coefficient Rho falling
between 0.1 and 0.3. The use of Spearman ranking
method results in higher correlation coefficient compared
to Pearson method (data not shown). Therefore, the
current associations could have been overestimated.
Reviewing the past literature, we find that most studies
also show very weak associations to diabetes indicators,
regardless of whether Pearson association or Spearman
ranking is being used [14, 24, 42]. Some previous studies
show stronger association but have limited participants
or the participants are older in age. Therefore, there are
no strong evidence to support proinsulin, insulin and
Figure 5. Association of proinsulin, insulin levels and proinsulin to insulin (P/I) ratio with Matsuda Index in normal, prediabetic and diabetic groups. (A) Negative association of fasting proinsulin levels with Matsuda Index in all subgroups with moderate strength. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language for each subgroup (normal in red, prediabetes in green and diabetes in blue). Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software. Shaded line, linear regression with 95% of confidence interval (CI). (B) Glucose-stimulated proinsulin levels were negatively associated with Matsuda index in all subgroups. (C) Fasting insulin levels were strongly associated with Matsuda index in all subgroups. (D) Glucose-stimulated insulin levels were strongly associated with Matsuda index in all subgroups. (E) Fasting P/I ratio was much weaker than proinsulin and insulin levels in association with Matsuda index in all subgroups. (F) Glucose-stimulated P/I ratio was much weaker than proinsulin and insulin levels in association with Matsuda index in all subgroups.
www.aging-us.com 10724 AGING
P/I ratio as good predictors of T2D development.
Consistently, ROC curve analysis of our cohort show that
proinsulin, insulin and P/I ratio are much less effective
than age as predictor of diabetes, with P/I ratio as the
worse among the three (Figure 6).
The exceptions are their associations with insulin
sensitivity as measured by Matsuda index (Figure 5 and
Supplementary Figure 4). Fasting and postprandial
insulin are both strongly associated with Matsuda index.
This is simply due to the fact that Matsuda index is
calculated based on both fasting and postprandial insulin
levels. What is interesting is that the fasting and
postprandial proinsulin levels have moderate and
negative association with Matsuda index in all subgroups,
suggesting that they could serve as potential predictors of
insulin sensitivity and hyperglycemia development.
Subgroup analysis reveals distinct and sometimes
opposite patterns of correlations
By careful examination of the scatterplot, we notice
in multiple cases that there is a trend of bimodal
distribution. For example, postprandial insulin is
trending positive at lower FPG but negative at higher
FPG (Figure 2D). This observation leads us to subgroup
the participants into normal, prediabetic and diabetic
group in the association studies. Interestingly, when
subgrouping the population, stronger associations
appear in certain subgroups, for example postprandial
insulin levels and P/I ratio with FPG in diabetic group
(Figure 2D, 2E), postprandial proinsulin, insulin and P/I
ratio with HbA1c in diabetic group (Figure 3B, 3D, 3E),
and postprandial insulin levels with postprandial glucose
levels in normal and prediabetic groups (Figure 4D).
These results suggest that these metabolic parameters
could be strong predictors of T2D development in
certain subgroups, but their prediction function awaits
further investigation and careful evaluation. To our
knowledge, our current study is the first of its kind to
systematically compare in each subgroup and the whole
population the correlations of proinsulin, insulin and P/I
ratio with diabetes parameters.
P/I ratio is worse than proinsulin or insulin levels in
predicting hyperglycemia development
Interestingly, although several previous studies suggest
that the P/I ratio is as good as or even better than
proinsulin or insulin in predicting T2D development
[43, 44], we find the opposite. Across multiple panels of
association study, we show that compared to proinsulin
and insulin, P/I ratio has the worse association with T2D
parameters including FPG, HbA1c, postprandial glucose
Figure 6. Prediction power of proinsulin, insulin levels and proinsulin to insulin (P/I) ratio for diabetes. (A) Weak prediction power of proinsulin, insulin levels and P/I ratio as compared to age and HbA1c. Data from 1579 individuals were plotted with pROC package in R. HbA1c was one of the three parameters used to define diabetes, therefore a strong predictor (curve to the upper left corner). The diagonal line means no prediction power. All proinsulin, insulin levels and P/I ratio were weaker than age in predicting diabetes and P/I ratio was the worse. (B) Weak prediction power of postprandial proinsulin, insulin levels and P/I ratio as compared to age and HbA1c. All proinsulin, insulin levels and P/I ratio after 2-hour glucose stimulation were weaker than age in predicting diabetes and P/I ratio was the worse.
www.aging-us.com 10725 AGING
and insulin sensitivity as measured by Matsuda index,
whether in the whole population or in the subgroups. We
confirm that this discrepancy is not due to the methods
in association study, as most previous reports also apply
the same software (SPSS) and the same methods
(Spearman or Pearson) and we find the P/I ratio remains
the least effective predictor by using Pearson analysis
(Data not shown). More striking difference is found in
the correlation to Matsuda index, where both proinsulin
and insulin levels, at both fasting and glucose-stimulated
conditions, are tightly correlated to Matsuda index, but
not the P/I ratio (Figure 5). Further, ROS analysis
confirm that P/I ratio is worse than proinsulin and
insulin in predicting diabetes (Figure 6). Our results
suggest that, as appose to several early proposals, defect
in processing of proinsulin to insulin is not likely a
critical contributing factor to T2D development. In
previous reports, the reported associations are also
generally weaker for P/I ratio as compared to that for
proinsulin or insulin [22, 23, 45–47]. Therefore, our
results are not inconsistent with the literature. Together,
our data argue against using P/I ratio in evaluation of
T2D development.
Despite the points mentioned above, there are several
caveats worth mentioning. First, our study is focused
on a specific group of people living in a restricted
area. The1579 individuals in our cohort study are all
East Asia ethnicity, living in an Island south to the
China mainland. Second, the participants were
recruited voluntarily and bias could be introduced
as those who were more or less concerned about
T2D were more likely to participate. These caveats,
among many others, suggest that care should be taken
when attempts to extrapolate our results to other
studies.
MATERIALS AND METHODS
Subjects
The current study is part of an ongoing project led by
Hainan General Hospital aiming to better understand
several health issues of Hainan Province, China. The
subjects are ethnically East Asian distributing across the
province, including cities and countryside. This study
includes 1579 men and women of different age,
socioeconomical status and education levels. Such
information was collected based on a survey before
admitting participants for glucose tolerance test and
biometrics measurement. The study was approved by
ethical committee of Hainan General Hospital and all
participants gave written informed consents. The data
collected in this manuscript are completely different
from a previous co-authored paper in Hainan Medical
Journal (Supplementary Data). The original data can be
found in Supplementary Data. The processed data can be
found in Supplementary Table 2.
Clinical measurements
Weight was measured by using mechanic scale and
height mechanical rod mounted on a wall. Values for
weight and height were kept to the nearest 0.1 kg and
0.5 cm, respectively. BMI was calculated based on
weight and height, e.g. dividing weight (kg) by the
square of height (m3). Waist was measured at the
midpoint between the lateral iliac crest and lowest rib to
the nearest 0.5 cm. Systolic pressure and diastolic
pressure were obtained by standard sphygmomanometer
and values were expressed in millimeters of mercury
(mmHg). Heartbeat was measured with a pulse oximeter
for 3 times and averaged.
Oral glucose tolerance test (OGTT)
A standard 2-hour, 75-gram oral glucose tolerance test
(OGTT) protocol was used in this study. Participants
were fasted overnight at least for 10 hours. First, blood
was drawn at the fasting state. Participants were then
required to drink 0.2 kg of a syrupy glucose solution that
contains 75 grams of sugar within 2 minutes. After 2
hours, a second blood draw was carried out. Glucose
levels, proinsulin levels and insulin levels in these
samples were then determined as follows: plasma
glucose was measured by using Hexokinase Activity
Assay Kit (Abcam). Insulin was determined by using
Human Insulin ELISA Kit (Abcam). Proinsulin was
measured by using Human proinsulin ELISA Kit
(Abcam).
Insulin sensitivity
Insulin sensitivity were evaluated by using Matsuda ISI,
which is generated by using the template published at
http://mmatsuda.diabetes-smc.jp/english.html, based on
fasting and 2-hour glucose-stimulated insulin levels and
plasma glucose levels. Detailed information regarding
the Matsuda index could be found in this website as
well as early publications [29, 30].
Data visualization
Graphs were generated using Graphpad prisim software
and Rstudio (Version 1.1.463) installed with ggplot2
and pROC package. For association visualization, data
were first converted by log10 to obtain normally
distributed data, then analyzed by using linear model
either across all data points or data points for normal,
prediabetic and diabetic groups. A small number of
outliers were removed for visualization purpose.
www.aging-us.com 10726 AGING
Statistical analysis
Statistical analyses and association studies were
conducted using IBM SPSS version 24. Association was
evaluated by binary variable model and correlation
coefficient factor Rho were obtained by using
spearman’s rank. In this paper, the absolute value of
coefficient factor Rho lower than 0.2 is considered not
associated, 0.21-0.4 weak association, 0.41-0.6 moderate
association, 0.61 and higher strong association. A linear
regression model was used to derive the standardized β
and P values for moderate or strong associations and the
results were adjusted to age, sex and body mass index
(BMI) as indicated in the main text.
CONFLICTS OF INTEREST
The authors have declared no conflicts of interest.
FUNDING
This study was supported by Hainan Provincial Key
Research and Development Project (ZDYF2018130) and
Hainan Medical Research Project (1801320249A2001).
REFERENCES
1. Caspersen CJ, Thomas GD, Boseman LA, Beckles GL, Albright AL. Aging, diabetes, and the public health system in the United States. Am J Public Health. 2012; 102:1482–97.
https://doi.org/10.2105/AJPH.2011.300616 PMID:22698044
2. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and Trends in Diabetes Among Adults in the United States, 1988-2012. JAMA. 2015; 314:1021–29.
https://doi.org/10.1001/jama.2015.10029 PMID:26348752
3. Longo M, Bellastella G, Maiorino MI, Meier JJ, Esposito K, Giugliano D. Diabetes and Aging: From Treatment Goals to Pharmacologic Therapy. Front Endocrinol (Lausanne). 2019; 10:45.
https://doi.org/10.3389/fendo.2019.00045 PMID:30833929
4. Kalyani RR, Golden SH, Cefalu WT. Diabetes and Aging: Unique Considerations and Goals of Care. Diabetes Care. 2017; 40:440–43.
https://doi.org/10.2337/dci17-0005 PMID:28325794
5. Czech MP. Insulin action and resistance in obesity and type 2 diabetes. Nat Med. 2017; 23:804–14.
https://doi.org/10.1038/nm.4350 PMID:28697184
6. Campbell IL, Harrison LC. Molecular pathology of type 1 diabetes. Mol Biol Med. 1990; 7:299–309.
PMID:2233244
7. Gastaldelli A. Role of beta-cell dysfunction, ectopic fat accumulation and insulin resistance in the pathogenesis of type 2 diabetes mellitus. Diabetes Res Clin Pract. 2011 (Suppl 1); 93:S60–65.
https://doi.org/10.1016/S0168-8227(11)70015-8 PMID:21864753
8. Steiner DF, Park SY, Støy J, Philipson LH, Bell GI. A brief perspective on insulin production. Diabetes Obes Metab. 2009 (Suppl 4); 11:189–96.
https://doi.org/10.1111/j.1463-1326.2009.01106.x PMID:19817801
9. Fu Z, Gilbert ER, Liu D. Regulation of insulin synthesis and secretion and pancreatic Beta-cell dysfunction in diabetes. Curr Diabetes Rev. 2013; 9:25–53.
https://doi.org/10.2174/157339913804143225 PMID:22974359
10. Liu M, Wright J, Guo H, Xiong Y, Arvan P. Proinsulin entry and transit through the endoplasmic reticulum in pancreatic beta cells. Vitam Horm. 2014; 95:35–62.
https://doi.org/10.1016/B978-0-12-800174-5.00002-8 PMID:24559913
11. Haataja L, Snapp E, Wright J, Liu M, Hardy AB, Wheeler MB, Markwardt ML, Rizzo M, Arvan P. Proinsulin intermolecular interactions during secretory trafficking in pancreatic β cells. J Biol Chem. 2013; 288:1896–906.
https://doi.org/10.1074/jbc.M112.420018 PMID:23223446
12. Wray GM, Foster SJ, Hinds CJ, Thiemermann C. A cell wall component from pathogenic and non-pathogenic gram-positive bacteria (peptidoglycan) synergises with endotoxin to cause the release of tumour necrosis factor-alpha, nitric oxide production, shock, and multiple organ injury/dysfunction in the rat. Shock. 2001; 15:135–42.
https://doi.org/10.1097/00024382-200115020-00010 PMID:11220642
13. Fritsche A, Madaus A, Stefan N, Tschritter O, Maerker E, Teigeler A, Häring H, Stumvoll M. Relationships among age, proinsulin conversion, and beta-cell function in nondiabetic humans. Diabetes. 2002 (Suppl 1); 51:S234–39.
https://doi.org/10.2337/diabetes.51.2007.S234 PMID:11815485
14. Kim NH, Kim DL, Choi KM, Baik SH, Choi DS. Serum insulin, proinsulin and proinsulin/insulin ratio in type 2 diabetic patients: as an index of beta-cell function or insulin resistance. Korean J Intern Med (Korean Assoc Intern Med). 2000; 15:195–201.
www.aging-us.com 10727 AGING
https://doi.org/10.3904/kjim.2000.15.3.195 PMID:11242807
15. Haffner SM, Gonzalez C, Mykkänen L, Stern M. Total immunoreactive proinsulin, immunoreactive insulin and specific insulin in relation to conversion to NIDDM: the Mexico City Diabetes Study. Diabetologia. 1997; 40:830–37.
https://doi.org/10.1007/s001250050756 PMID:9243105
16. Bryhni B, Arnesen E, Jenssen TG. Associations of age with serum insulin, proinsulin and the proinsulin-to-insulin ratio: a cross-sectional study. BMC Endocr Disord. 2010; 10:21.
https://doi.org/10.1186/1472-6823-10-21 PMID:21162746
17. Heaton DA, Millward BA, Gray IP, Tun Y, Hales CN, Pyke DA, Leslie RD. Increased proinsulin levels as an early indicator of B-cell dysfunction in non-diabetic twins of type 1 (insulin-dependent) diabetic patients. Diabetologia. 1988; 31:182–84.
https://doi.org/10.1007/BF00276853 PMID:3286344
18. Pfützner A, Kunt T, Hohberg C, Mondok A, Pahler S, Konrad T, Lübben G, Forst T. Fasting intact proinsulin is a highly specific predictor of insulin resistance in type 2 diabetes. Diabetes Care. 2004; 27:682–87.
https://doi.org/10.2337/diacare.27.3.682 PMID:14988285
19. Wareham NJ, Byrne CD, Williams R, Day NE, Hales CN. Fasting proinsulin concentrations predict the development of type 2 diabetes. Diabetes Care. 1999; 22:262–70.
https://doi.org/10.2337/diacare.22.2.262 PMID:10333943
20. Kahn SE, Leonetti DL, Prigeon RL, Boyko EJ, Bergstom RW, Fujimoto WY. Proinsulin levels predict the development of non-insulin-dependent diabetes mellitus (NIDDM) in Japanese-American men. Diabet Med. 1996 (Suppl 6); 13:S63–66.
https://doi.org/10.1002/dme.1996.13.s6.63 PMID:8894485
21. Zethelius B, Byberg L, Hales CN, Lithell H, Berne C. Proinsulin and acute insulin response independently predict Type 2 diabetes mellitus in men—report from 27 years of follow-up study. Diabetologia. 2003; 46:20–26.
https://doi.org/10.1007/s00125-002-0995-2 PMID:12637978
22. Mykkänen L, Haffner SM, Hales CN, Rönnemaa T, Laakso M. The relation of proinsulin, insulin, and proinsulin-to-insulin ratio to insulin sensitivity and acute insulin response in normoglycemic subjects. Diabetes. 1997; 46:1990–95.
https://doi.org/10.2337/diab.46.12.1990 PMID:9392485
23. Haffner SM, Mykkänen L, Valdez RA, Stern MP, Holloway DL, Monterrosa A, Bowsher RR. Disproportionately increased proinsulin levels are associated with the insulin resistance syndrome. J Clin Endocrinol Metab. 1994; 79:1806–10.
https://doi.org/10.1210/jcem.79.6.7989488 PMID:7989488
24. Vangipurapu J, Stančáková A, Kuulasmaa T, Kuusisto J, Laakso M. Both fasting and glucose-stimulated proinsulin levels predict hyperglycemia and incident type 2 diabetes: a population-based study of 9,396 Finnish men. PLoS One. 2015; 10:e0124028.
https://doi.org/10.1371/journal.pone.0124028 PMID:25853252
25. Birkeland KI, Torjesen PA, Eriksson J, Vaaler S, Groop L. Hyperproinsulinemia of type II diabetes is not present before the development of hyperglycemia. Diabetes Care. 1994; 17:1307–10.
https://doi.org/10.2337/diacare.17.11.1307 PMID:7821172
26. Schuit FC, In’t Veld PA, Pipeleers DG. Glucose stimulates proinsulin biosynthesis by a dose-dependent recruitment of pancreatic beta cells. Proc Natl Acad Sci USA. 1988; 85:3865–69.
https://doi.org/10.1073/pnas.85.11.3865 PMID:3287379
27. Mako ME, Starr JI, Rubenstein AH. Circulating proinsulin in patients with maturity onset diabetes. Am J Med. 1977; 63:865–69.
https://doi.org/10.1016/0002-9343(77)90538-1 PMID:343587
28. Wang PW, Abbasi F, Carantoni M, Chen YD, Azhar S, Reaven GM. Insulin resistance does not change the ratio of proinsulin to insulin in normal volunteers. J Clin Endocrinol Metab. 1997; 82:3221–24.
https://doi.org/10.1210/jc.82.10.3221 PMID:9329342
29. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999; 22:1462–70.
https://doi.org/10.2337/diacare.22.9.1462 PMID:10480510
30. DeFronzo RA, Matsuda M. Reduced time points to calculate the composite index. Diabetes Care. 2010; 33:e93.
https://doi.org/10.2337/dc10-0646 PMID:20587713
31. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004 (Suppl 1); 27:S5–10.
www.aging-us.com 10728 AGING
https://doi.org/10.2337/diacare.27.2007.S5 PMID:14693921
32. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care. 2010 (Suppl 1); 33:S11–61.
https://doi.org/10.2337/dc10-S011 PMID:20042772
33. El Shabrawy AM, Elbana KA, Abdelsalam NM. Proinsulin/insulin ratio as a predictor of insulin resistance and B-cell dysfunction in obese Egyptians ((insulin resistance & B-cell dysfunction in obese Egyptians)). Diabetes Metab Syndr. 2019; 13:2094–96.
https://doi.org/10.1016/j.dsx.2019.04.044 PMID:31235142
34. Nomura K, Inoue K, Akimoto K. A two-step screening, measurement of HbA1c in association with FPG, may be useful in predicting diabetes. PLoS One. 2012; 7:e36309.
https://doi.org/10.1371/journal.pone.0036309 PMID:22558430
35. Gillett MJ. International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes: Diabetes Care 2009; 32(7): 1327-1334. Clin Biochem Rev. 2009; 30:197–200.
PMID:20011212
36. Rushforth NB, Miller M, Bennett PH. Fasting and two-hour post-load glucose levels for the diagnosis of diabetes. The relationship between glucose levels and complications of diabetes in the Pima Indians. Diabetologia. 1979; 16:373–79.
https://doi.org/10.1007/BF01223157 PMID:467847
37. Pradhan AD, Manson JE, Meigs JB, Rifai N, Buring JE, Liu S, Ridker PM. Insulin, proinsulin, proinsulin:insulin ratio, and the risk of developing type 2 diabetes mellitus in women. Am J Med. 2003; 114:438–44.
https://doi.org/10.1016/S0002-9343(03)00061-5 PMID:12727576
38. Kahn SE, Leonetti DL, Prigeon RL, Boyko EJ, Bergstrom RW, Fujimoto WY. Relationship of proinsulin and insulin with noninsulin-dependent diabetes mellitus and coronary heart disease in Japanese-American men: impact of obesity—clinical research center study. J Clin Endocrinol Metab. 1995; 80:1399–406.
https://doi.org/10.1210/jcem.80.4.7714116 PMID:7714116
39. Kahn SE, Leonetti DL, Prigeon RL, Boyko EJ, Bergstrom RW, Fujimoto WY. Proinsulin as a marker for the development of NIDDM in Japanese-American men. Diabetes. 1995; 44:173–79.
https://doi.org/10.2337/diab.44.2.173 PMID:7859937
40. Hanley AJ, D’Agostino R Jr, Wagenknecht LE, Saad MF, Savage PJ, Bergman R, Haffner SM, Insluin Resistance Atrherosclerosis S, and Insluin Resistance Atrherosclerosis Study. Increased proinsulin levels and decreased acute insulin response independently predict the incidence of type 2 diabetes in the insulin resistance atherosclerosis study. Diabetes. 2002; 51:1263–70.
https://doi.org/10.2337/diabetes.51.4.1263 PMID:11916954
41. Nijpels G, Popp-Snijders C, Kostense PJ, Bouter LM, Heine RJ. Fasting proinsulin and 2-h post-load glucose levels predict the conversion to NIDDM in subjects with impaired glucose tolerance: the Hoorn Study. Diabetologia. 1996; 39:113–18.
https://doi.org/10.1007/BF00400421 PMID:8720611
42. Mykkänen L, Zaccaro DJ, Hales CN, Festa A, Haffner SM. The relation of proinsulin and insulin to insulin sensitivity and acute insulin response in subjects with newly diagnosed type II diabetes: the Insulin Resistance Atherosclerosis Study. Diabetologia. 1999; 42:1060–66.
https://doi.org/10.1007/s001250051271 PMID:10447516
43. Larsson H, Ahrén B. Relative hyperproinsulinemia as a sign of islet dysfunction in women with impaired glucose tolerance. J Clin Endocrinol Metab. 1999; 84:2068–74.
https://doi.org/10.1210/jc.84.6.2068 PMID:10372712
44. Saisho Y, Maruyama T, Hirose H, Saruta T. Relationship between proinsulin-to-insulin ratio and advanced glycation endproducts in Japanese type 2 diabetic subjects. Diabetes Res Clin Pract. 2007; 78:182–88.
https://doi.org/10.1016/j.diabres.2007.03.014 PMID:17467843
45. Yoshioka N, Kuzuya T, Matsuda A, Taniguchi M, Iwamoto Y. Serum proinsulin levels at fasting and after oral glucose load in patients with type 2 (non-insulin-dependent) diabetes mellitus. Diabetologia. 1988; 31:355–60.
https://doi.org/10.1007/BF02341503 PMID:3046976
46. Lorenzo C, Hanley AJ, Rewers MJ, Haffner SM. Disproportionately elevated proinsulinemia is observed at modestly elevated glucose levels within the normoglycemic range. Acta Diabetol. 2014; 51:617–23.
https://doi.org/10.1007/s00592-014-0565-3 PMID:24532116
www.aging-us.com 10729 AGING
47. Røder ME, Porte D Jr, Schwartz RS, Kahn SE. Disproportionately elevated proinsulin levels reflect the degree of impaired B cell secretory capacity in patients with noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1998; 83:604–08.
https://doi.org/10.1210/jc.83.2.604 PMID:9467581
www.aging-us.com 10730 AGING
SUPPLEMENTARY MATERIALS
Supplementary Figures
Supplementary Figure 1. (related to Figure 2). Weak to no association of fasting proinsulin, fasting insulin levels and fasting proinsulin to insulin (P/I) ratio with fasting plasma glucose (FPG) in the whole population. (A) Fasting proinsulin was weakly associated with FPG in the whole population and better than insulin and P/I ratio in predicting diabetes. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language. Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software, with absolute Rho < 0.3 considered weak, 0.3≤Rho<0.6 moderate and Rho≥0.6 strong association; ns, not significant. (B) Glucose-stimulated proinsulin levels was weakly associated with FPG in the whole population. (C) Fasting insulin levels was weakly associated with FPG in the whole population. (D) Glucose-stimulated insulin had no association with FPG in the whole population. (E) Fasting P/I ratio was weakly associated with FPG in the whole population. (F) Glucose-stimulated P/I ratio was not significantly associated with FPG in the whole population.
www.aging-us.com 10731 AGING
Supplementary Figure 2. (related to Figure 3). Weak to no association of fasting proinsulin, fasting insulin levels and fasting proinsulin to insulin (P/I) ratio with diabetic indicator hemoglobin A1c (HbA1c) in the whole population. (A) Fasting proinsulin had very weak association with HbAc1 in the whole population. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language. Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software, with absolute Rho < 0.3 considered weak, 0.3≤Rho<0.6 moderate and Rho≥0.6 strong association; ns, not significant. (B) Proinsulin levels after 2-hour glucose stimulation in an oral glucose tolerance test (OGTT) showed no significant association with HbA1c in the whole population. (C) Fasting insulin levels was not significantly associated with HbA1c in the whole population. (D) Insulin levels after 2-hour OGTT had negative and weak association with HbA1c in the whole population. (E) No significant association of fasting P/I ratio with HbA1c in the whole population. (F) Weak and negative association of glucose-stimulated P/I ratio with HbA1c in the whole population.
www.aging-us.com 10732 AGING
Supplementary Figure 3. (related to Figure 4) Association of fasting proinsulin, fasting insulin levels and fasting proinsulin to insulin (P/I) ratio with diabetic indicator 2-hour glucose levels after an oral glucose tolerance test (OGTT2hPG) in the whole population. (A) Fasting proinsulin had weak association with OGTT2hPG in the whole population. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language. Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software, with absolute Rho < 0.3 considered weak, 0.3≤Rho<0.6 moderate and Rho≥0.6 strong association; ns, not significant. (B) Proinsulin levels after OGTT showed weak association with OGTT2hPG in the whole population. (C) Fasting insulin levels was weakly associated with OGTT2hPG in the whole population. (D) Insulin levels after OGTT had moderate (Rho =0.434) association with OGTT2hPG in the whole population. (E) No significant association of fasting P/I ratio with OGTT2hPG in the whole population. (F) Weak and negative association of glucose-stimulated P/I ratio with OGTT2hPG in the whole population.
www.aging-us.com 10733 AGING
Supplementary Figure 4. (related to Figure 5) Association of fasting proinsulin, fasting insulin levels and fasting proinsulin to insulin (P/I) ratio with insulin sensitivity index Matsuda index in the whole population. (A) Fasting proinsulin had negative association with Matsuda index in the whole population. Data from 1579 participants were log transformed, scatter plotted and linear modeled by using R language. Association strength was evaluated by Spearman's association coefficient (Rho) using SPSS software, with absolute Rho < 0.3 considered weak, 0.3≤Rho<0.6 moderate and Rho≥0.6 strong association; ns, not significant. (B) Proinsulin levels after glucose stimulation in an oral glucose tolerance test (OGTT) had moderate association with Matsuda index in the whole population. (C) Fasting insulin levels was strongly associated with Matsuda index in the whole population. (D) Insulin levels after glucose stimulation in OGTT strongly associated with Matsuda index in the whole population. (E) Weak association of fasting P/I ratio with Matsuda index in the whole population. (F) Weak association of glucose-stimulated P/I ratio with Matsuda index in the whole population.
www.aging-us.com 10734 AGING
Supplementary Tables
Supplementary Table 1. Physical and metabolic characteristics of 1579 participants in this study in Hainan province, China.
Normal Prediabetes P value Diabetes P value
Whole (n = 1579) N= 639 N= 705 N= 235
Age(year) 41.69±12.33 50.91±12.31 .00001 56.75±11.11 .00001
Weight(Kg) 58.40±10.79 61.35±11.32 .00001 63.10±10.03 .00001
Height(cm) 159.49±7.86 159.23±8.55 .5614 159.36±8.47 .8376
Body mass index (BMI, Kg/m2) 22.91±3.56 24.09±3.39 .00001 24.80±3.09 .00001
Waist circumference(cm) 77.95±9.40 82.72±9.40 .00001 86.11±8.27 .00001
Systolic pressure (mmHg) 117.24±16.83 127.81±19.48 .00001 132.56±21.01 .00001
Diastolic pressure(mmHg) 74.92±11.13 79.41±12.44 .00001 80.62±12.55 .00001
Heartbeat(times/min) 79.73±11.07 82.44±12.63 .00001 84.36±12.80 .00001
FPG(mmol/L) 4.87±0.38 5.35±0.52 .00001 7.52±2.78 .00001
OGTT2PG(mmol/L) 5.89±1.06 7.68±1.60 .00001 13.38±5.01 .00001
Triglyceride(mmol/L) 1.53±1.04 2.03±1.79 .00001 2.61±3.34 .00001
Total cholesterol(mmol/L) 5.15±0.97 5.55±1.07 .00001 5.78±1.27 .00001
LDL(mmol/L) 2.78±0.76 3.04±0.84 .00001 3.16±0.92 .00001
HDL(mmol/L) 1.54±0.41 1.49±0.32 .0124 1.42±0.29 .00001
Blood urine acid(μmol/L) 336.40±85.68 367.00±89.86 .00001 374.16±88.96 .00001
HbA1c(%) 5.22±0.32 5.68±0.39 .00001 7.08±1.74 .00001
VitaminD3(ng/mL) 36.38±10.48 38.55±10.86 .0002 39.57±11.12 .00001
Fasting proinsulin(pmol/L) 11.56±8.14 13.47±10.63 .0002 21.91±23.82 .00001
2h proinsulin(pmol/L) 57.80±50.26 62.99±51.66 .0626 74.52±63.44 .00001
Fasting insulin(pmol/L) 57.53±34.03 67.62±44.18 .00001 86.69±84.84 .00001
2h insulin(pmol/L) 379.99±295.46 572.72±491.65 .00001 600.48±570.17 .00001
Male (n = 567) N = 202 N= 261 N = 104
Age(year) 43.56±12.40 50.81±12.30 .00001 55.86±11.60 .00001
Weight(Kg) 66.08±9.12 68.82±10.58 .0036 68.37±9.48 .041
Height(cm) 167.02±6.08 167.02±6.13 .99999 166.05±6.02 .1858
Body mass index (BMI, Kg/m2) 23.70±2.80 24.60±3.19 .0016 24.77±3.00 .0022
Waist circumference(cm) 83.84±8.16 87.32±9.05 .00001 88.25±7.39 .00001
Systolic pressure (mmHg) 123.11±15.60 130.72±18.09 .00001 132.11±19.79 .00001
Diastolic pressure(mmHg) 77.71±11.28 82.76±13.11 .00001 81.63±12.00 .0052
Heartbeat(times/min) 77.41±10.62 80.16±10.93 .0068 81.90±12.60 .0012
FPG(mmol/L) 4.87±0.39 5.33±0.51 .00001 7.46±3.20 .00001
OGTT2hPG(mmol/L) 5.89±1.16 7.61±1.72 .00001 13.33±5.16 .00001
Triglyceride(mmol/L) 1.90±1.40 2.33±2.18 .015 2.81±3.23 .0006
Total cholesterol(mmol/L) 5.34±0.98 5.53±1.06 5.62±1.08
LDL(mmol/L) 3.00±0.79 3.07±0.88 .3754 3.08±0.92 .4286
HDL(mmol/L) 1.40±0.29 1.37±0.28 .261 1.35±0.26 .1404
Blood urine acid(μmol/L) 412.34±81.38 428.23±79.52 .0354 410.17±78.38 .8232
HbA1c(%) 5.25±0.33 5.70±0.37 .00001 7.02±1.80 .00001
VitaminD3(ng/mL) 43.20±11.24 44.32±12.24 .3122 44.62±11.87 .3052
Fasting proinsulin(pmol/L) 13.88±10.21 15.93±12.02 .0528 26.47±30.65 .00001
2h proinsulin(pmol/L) 68.46±68.43 73.12±56.15 .4214 84.22±73.43 .0636
Fasting insulin(pmol/L) 58.44±32.59 68.51±45.56 .0082 78.87±67.75 .0004
www.aging-us.com 10735 AGING
2h insulin(pmol/L) 381.00±313.10 566.37±499.41 .00001 578.36±533.82 .00001
Female (n = 1012) N= 437 N= 444 N = 131
Age(year) 40.83±12.21 50.97±12.33 .00001 57.45±10.70 .00001
Weight(Kg) 54.85±9.59 56.96±9.27 .001 58.92±8.37 .00001
Height(cm) 156.03±5.94 154.65±6.12 .0008 154.04±6.01 .0008
Body mass index (BMI, Kg/m2) 22.54±3.81 23.79±3.46 .00001 24.81±3.16 .00001
Waist circumference(cm) 75.22±8.67 80.02±8.53 .00001 84.41±8.56 .00001
Systolic pressure (mmHg) 114.53±16.70 126.11±20.09 .00001 132.91±22.00 .00001
Diastolic pressure(mmHg) 73.64±10.82 77.44±11.60 .00001 79.83±12.95 .00001
Heartbeat(times/min) 80.81±11.13 83.79±13.36 .0004 86.35±12.67 .00001
FPG(mmol/L) 4.86±0.38 5.35±0.52 .00001 7.56±2.40 .00001
OGTT2hPG(mmol/L) 5.89±1.01 7.72±1.53 .00001 13.42±4.90 .00001
Triglyceride(mmol/L) 1.36±0.76 1.84±1.48 .00001 2.44±3.43 .00001
Total cholesterol(mmol/L) 5.06±0.95 5.56±1.08 .00001 5.91±1.40 .00001
LDL(mmol/L) 2.68±0.73 3.02±0.81 .00001 3.23±0.92 .00001
HDL(mmol/L) 1.60±0.44 1.56±0.32 .3342 1.48±0.29 .0034
Blood urine acid(μmol/L) 301.30±61.52 331.15±75.05 .00001 345.57±86.70 .00001
HbA1c(%) 5.21±0.32 5.67±0.40 .00001 7.13±1.69 .00001
VitaminD3(ng/mL) 33.22±8.42 35.17±8.27 .0006 35.55±8.62 .006
Fasting proinsulin(pmol/L) 10.48±6.72 12.03±9.44 .0052 18.28±15.72 .00001
2h proinsulin(pmol/L) 52.87±38.15 57.03±47.89 .1546 66.81±53.26 .001
Fasting insulin(pmol/L) 57.12±34.70 67.10±43.38 .0002 92.89±96.08 .00001
2h insulin(pmol/L) 379.52±287.27 576.45±487.55 .00001 618.05±598.91 .00001
Please browse Full Text version to see the data of Supplementary Table 2.
Supplemental Table 2. Data used in this study after removing invalid data points.
Supplementary Data
Please browse Full Text version to see the data of Supplementary evidence of original data.